GlimpseData: towards continuous vision-based personal analytics
GlimpseData: towards continuous vision-based personal analytics
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GlimpseData：迈向基于视觉的持续个人分析
DOI：
10.1145/2611264.2611269
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
D. Wetherall
中科院分区：
文献类型：
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作者：
Seungyeop Han;R. Nandakumar;Matthai Philipose;A. Krishnamurthy;D. Wetherall
新兴的可穿戴设备为移动的上下文感知应用提供了使用连续音频/视频感测数据作为原始输入的新机会。由于输入的高数据率和计算密集型特性，设计高效的框架和应用程序非常重要。我们提出了GlimpseData框架来收集和分析用于研究连续高数据率移动的感知的数据。作为一个案例研究，我们表明，我们可以使用低功耗的传感器作为过滤器，以避免传感和处理视频的人脸检测。我们相对简单的机制避免了处理大约60%的视频帧，同时只丢失了10%的人脸帧。
Emerging wearable devices provide a new opportunity for mobile context-aware applications to use continuous audio/video sensing data as primitive inputs. Due to the high-datarate and compute-intensive nature of the inputs, it is important to design frameworks and applications to be efficient. We present the GlimpseData framework to collect and analyze data for studying continuous high-datarate mobile perception. As a case study, we show that we can use low-powered sensors as a filter to avoid sensing and processing video for face detection. Our relatively simple mechanism avoids processing roughly 60% of video frames while missing only 10% of frames with faces in them.